scholarly journals Central nervous system medication use and incident mobility limitation in community elders: the health, aging, and body composition study

2009 ◽  
Vol 18 (10) ◽  
pp. 916-922 ◽  
Author(s):  
Robert M. Boudreau ◽  
Joseph T. Hanlon ◽  
Yazan F. Roumani ◽  
Stephanie A. Studenski ◽  
Christine M. Ruby ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Body Composition ◽  
Medication Use ◽  
Mobility Limitation ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study

scholarly journals Vitamin K Status and Mobility Limitation and Disability in Older Adults: The Health, Aging, and Body Composition Study

2019 ◽  
Vol 75 (4) ◽  
pp. 792-797 ◽  
Author(s):  
M Kyla Shea ◽  
Stephen B Kritchevsky ◽  
Richard F Loeser ◽  
Sarah L Booth
Keyword(s):  
Body Composition ◽  
Vitamin K ◽  
Underlying Mechanism ◽  
Mobility Limitation ◽  
Mobility Disability ◽  
Body Composition Study ◽  
Increased Risk ◽  
Health Aging ◽  
Cox Proportional Hazard Models ◽  
Composition Study

Abstract Background Vitamin K has been implicated in chronic diseases associated with increased risk for mobility disability, such as osteoarthritis and cardiovascular disease. However, the association between vitamin K status and mobility disability is unknown. Therefore, we examined the association between vitamin K status and incident mobility disability in the Health, Aging, and Body Composition Study. Methods Plasma phylloquinone (vitamin K1) was categorized as <0.5, 0.5–<1.0 and ≥1.0 nmol/L (n = 1,323, 48% male). Plasma ucMGP, which increases when vitamin K status is low, was measured in 716 participants and categorized into tertiles. Mobility limitation and disability, defined as two consecutive semiannual reports of having any or a lot of difficulty walking a one-fourth mile or climbing 10 steps without resting, were assessed over a median 6–10 years of follow-up. Multivariate Cox proportional hazard models were used to evaluate the association between vitamin K status and incident mobility limitation and disability. Results Participants with plasma phylloquinone less than 0.5 nmol/L were more likely to develop mobility limitation and disability compared to those with at least 1.0 nmol/L (adjusted HR (95% CI) mobility limitation: 1.27 (1.05–1.53); disability: 1.34 (1.01–1.76)). After further adjustment for knee pain, the associations were partially attenuated (HR (95% CI) mobility limitation: 1.20 (0.99–1.45); disability: 1.26 (0.96–1.67)). Plasma ucMGP was not associated with incident mobility limitation, but was nonlinearly associated with incident mobility disability (HR (95% CI), compared to tertile 1: tertile 2 = 1.64 (1.19–2.27), tertile 3 = 1.17 (0.83–1.66), fully adjusted). Conclusion Our results suggest vitamin K may be involved in the disablement process in older age. Future studies are needed to confirm our findings and clarify the underlying mechanism.

scholarly journals All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 487-487
Author(s):  
Chenkai Wu ◽  
Xurui Jin
Keyword(s):  
Machine Learning ◽  
Body Composition ◽  
Risk Prediction ◽  
Risk Score ◽  
Body Composition Study ◽  
C Statistic ◽  
Dementia Risk ◽  
Health Aging ◽  
Composition Study ◽  
Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

scholarly journals Glycemic Status and Incident Heart Failure in Elderly Without History of Diabetes Mellitus: The Health, Aging, and Body Composition Study

2009 ◽  
Vol 15 (7) ◽  
pp. 593-599 ◽  
Author(s):  
Andreas Kalogeropoulos ◽  
Vasiliki Georgiopoulou ◽  
Tamara B. Harris ◽  
Stephen B. Kritchevsky ◽  
Douglas C. Bauer ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Body Composition ◽  
Glycemic Status ◽  
Body Composition Study ◽  
Health Aging ◽  
History Of ◽  
Incident Heart Failure ◽  
Composition Study
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